It is a very simple algorithm. For a relatively small training dataset, if features are independent, naive Bayes will usually perform well. For a large dataset, naive Bayes will still work well as feature independence can be assumed in this case regardless of the truth. Training of naive Bayes is usually faster than any other algorithms due to its computational simplicity. However, this may lead to high bias (low variance though).
- Credits
- Preface
- Getting Started with Python and Machine Learning
- What is machine learning and why do we need it?
- A very high level overview of machine learning
- A brief history of the development of machine learning algorithms
- Generalizing with data
- Overfitting, underfitting and the bias-variance tradeoff
- Avoid overfitting with feature selection and dimensionality reduction
- Preprocessing, exploration, and feature engineering
- Combining models
- Installing software and setting up
- Troubleshooting and asking for help
- Summary
- Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
- Spam Email Detection with Naive Bayes
- News Topic Classification with Support Vector Machine
- Click-Through Prediction with Tree-Based Algorithms
- Click-Through Prediction with Logistic Regression
- Stock Price Prediction with Regression Algorithms
- Best Practices
- Machine learning workflow
- Best practices in the data preparation stage
- Best practices in the training sets generation stage
- Best practice 5 - determine categorical features with numerical values
- Best practice 6 - decide on whether or not to encode categorical features
- Best practice 7 - decide on whether or not to select features and if so, how
- Best practice 8 - decide on whether or not to reduce dimensionality and if so how
- Best practice 9 - decide on whether or not to scale features
- Best practice 10 - perform feature engineering with domain expertise
- Best practice 11 - perform feature engineering without domain expertise
- Best practice 12 - document how each feature is generated
- Best practices in the model training, evaluation, and selection stage
- Best practices in the deployment and monitoring stage
- Summary
Naive Bayes
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